Disentangling reporting and disease transmission

O’Dea, Eamon B.; Drake, John M.

doi:10.1101/234658

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…Additionally, further work would be to include a heterogeneous ensemble as suggested by O’Dea et al [4], whereby all parameters are sampled randomly for each realisation rather than being equal. This will lead to more realistic results, as each parameter sample represents time series data from different locations, as suggested by studies on spatial statistics, a promising method for addressing limited data [8, 17, 18].…”

Section: Discussionmentioning

confidence: 99%

“…Finally we consider the SIS model with external infection which has been used to investigate EWS in prevalence and in incidence [4, 5]. We demonstrate how our analytical results compare for this system, and illustrate differences when applied to disease elimination.…”

Section: Prevalence Theorymentioning

confidence: 99%

“…Prior work from O’Dea et al [4] has also incorporated under-reporting using a negative binomial distribution; this can be included in this model when the rate λ ( t ) is gamma distributed.…”

Section: Incidence Theorymentioning

confidence: 99%

“…Simulation-based studies exploring incidence-type data have suggested that the potential for emergence of an infectious disease can be informed by statistical signatures [3, 4]. These studies represent the first attempts to understand the robustness of some indicators when used with disease emergence incidence data, subject to underreporting and time aggregation.…”

Section: Introductionmentioning

confidence: 99%

“…O’Dea et al [4] incorporate an observation model into a Birth-Death-Immigration (BDI) process to present an analytical study of EWS of disease emergence. This model allows prediction of the behaviour of EWS for dynamics captured by a BDI process but is not suitable for diseases with population-level immunity.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Southall

Dyson

Tildesley

2020

Preprint

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Early warning signals (EWS) identify systems approaching a critical transition, where the system undergoes a sudden change in state. For example, monitoring changes in variance or autocorrelation offers a computationally inexpensive method which can be used in real-time to assess when an infectious disease transitions to elimination. EWS have a promising potential to not only be used to monitor infectious diseases, but also to inform control policies to aid disease elimination. Previously, potential EWS have been identified for prevalence data, however the prevalence of a disease is often not known directly. In this work we identify EWS for incidence data, the standard data type collected by the Centers for Disease Control and Prevention (CDC) or World Health Organization (WHO). We show, through several examples, that EWS calculated on simulated incidence time series data exhibit vastly different behaviours to those previously studied on prevalence data. In particular, the variance displays a decreasing trend on the approach to disease elimination, contrary to that expected from critical slowing down theory; this could lead to unreliable indicators of elimination when calculated on real-world data. We derive analytical predictions which can be generalised for many epidemiological systems, and we support our theory with simulated studies of disease incidence. Additionally, we explore EWS calculated on the rate of incidence over time, a property which can be extracted directly from incidence data. We find that although incidence might not exhibit typical critical slowing down properties before a critical transition, the rate of incidence does, presenting a promising new data type for the application of statistical indicators. Author summaryThe threat posed by infectious diseases has a huge impact on our global society. It is therefore critical to monitor infectious diseases as new data become available during control campaigns. One obstacle in observing disease emergence or elimination is understanding what influences noise in the data and how this fluctuates when near to zero cases. The standard data type collected is the number of new cases per day/month/year but mathematical modellers often focus on data such as the total number of infectious people, due to its analytical properties. We have developed a methodology to monitor the standard type of data to inform whether a disease is approaching emergence or disease elimination. We have shown computationally how

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Section: Discussionmentioning

confidence: 99%

Section: Prevalence Theorymentioning

confidence: 99%

Section: Incidence Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Southall

Dyson

Tildesley

2020

Preprint

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Performance of early warning signals for disease re-emergence: a case study on COVID-19 data

Proverbio

Kemp

Magni

2021

Preprint

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The sudden emergence of infectious diseases pose threats to societies worldwide and it is notably difficult to detect. In the past few years, several early warning signals (EWS) were introduced, to alert to impending critical transitions and extend the set of indicators for risk assessment. While they were originally thought to be generic, recent works demonstrated their sensitivity to some dynamical characteristics such as system noise and rates of approach to critical parameter values. Moreover, testing on empirical data is so far limited. Hence, validating their performance remains a challenge. In this study, we analyse the performance of common EWS such as increasing variance and autocorrelation in detecting the emergence of COVID-19 outbreaks in various countries, based on prevalence data. We show that EWS are successful in detecting disease emergence provided that some basic assumptions are satisfied: a slow forcing through the transitions and not fat-tailed noise. We also show cases where EWS fail, thus providing a verification analysis of their potential and limitations. Overall, this suggests that EWS can be useful for active monitoring of epidemic dynamics, but that their performance is sensitive to surveillance procedures. Our results thus represent a further step towards the application of EWS indicators for informing public health policies.

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Dynamical footprints enable detection of disease emergence

Brett

Rohani

2020

PLoS Biol

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Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.

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Disentangling reporting and disease transmission

Cited by 3 publications

References 22 publications

Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data

Performance of early warning signals for disease re-emergence: a case study on COVID-19 data

Dynamical footprints enable detection of disease emergence

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